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Statistically Discriminative Sub-trajectory Mining with Multiple Testing Correction

Published: 05 November 2019 Publication History

Abstract

We propose a novel statistical approach to evaluate the statistical significance (reliability) of the findings in the discriminative sub-trajectory mining problem, called Statistically Discriminative Sub-trajectory Mining (Stat-DSM). Given two groups of trajectories, the goal is to extract moving patterns in the form of sub-trajectories that occur statistically significantly more often in one group than in the other. An advantage of the Stat-DSM method is that the statistical significance of the extracted sub-trajectories are properly controlled in the sense that the probability of finding a false discriminative sub-trajectory is smaller than a specified significance threshold a (e.g., 0.05). We conduct experiments on real-world datasets to demonstrate the effectiveness of the Stat-DSM method.

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cover image ACM Conferences
SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
November 2019
648 pages
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2019

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Author Tags

  1. Trajectory mining
  2. multiple testing
  3. significant pattern mining
  4. statistical testing

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  • Poster
  • Research
  • Refereed limited

Funding Sources

  • RIKEN Center for Advanced Intelligence Project
  • MEXT KAKENHI
  • JST CREST

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SIGSPATIAL '19
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SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%

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